Analysis of the State of High-Voltage Current Transformers Based on Gradient Boosting on Decision Trees

This paper addresses the problem of instrument current transformers technical state assessment based on machine learning methods. The introductory parts of the paper provide a detailed analysis of modern methods and approaches for technical state assessment of high-voltage power equipment of power p...

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Bibliographic Details
Published in:IEEE transactions on power delivery 2021-08, Vol.36 (4), p.2154-2163
Main Authors: Khalyasmaa, Alexandra I., Senyuk, Mihail D., Eroshenko, Stanislav A.
Format: Article
Language:English
Subjects:
Algorithms
Classification
Current transformers
Decision analysis
Decision trees
Feature extraction
gradient boosting
High voltages
high-voltage equipment
Machine learning
Maintenance engineering
Mathematical problems
Monitoring
Observability
Power generation
Power plants
random forest
Software
Software development tools
Substations
technical state assessment
Transformers
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Summary:This paper addresses the problem of instrument current transformers technical state assessment based on machine learning methods. The introductory parts of the paper provide a detailed analysis of modern methods and approaches for technical state assessment of high-voltage power equipment of power plants and substations as well as a review of modern software tools and the latest trends in the given field of study. Justification of the relevance of the presented research aimed at instrument current transformers technical state assessment is provided along with the motivation for machine learning methods application for improvement of the accuracy and quality of high-voltage equipment state classification. Within the framework of the study, a comparative analysis of gradient boosting on decision trees and random forest algorithms was carried out for a given mathematical problem formulation. The main stages of processing the initial dataset are proposed as a step-by-step procedure, including feature extraction, feature transformation, feature interactions, etc. The outperforming efficiency of gradient boosting on decision trees algorithm was validated for real power equipment fleet. The resulting classification quality metrics of current transformers technical state assessment, Precision and Recall, are estimated to be 87.1% and 83.7%, correspondingly.
ISSN:0885-8977
1937-4208
DOI:10.1109/TPWRD.2020.3021702